Intuit, the worldwide fintech leader behind TurboTax, Credit Karma, QuickBooks and Mailchimp, is expanding its security backbone. The company is looking for a Staff Machine Learning Engineer who will own the data and platform layer that powers real-time risk decisions across all of Intuit’s financial products. Based in Mountain View, California, the role sits at the intersection of data engineering, model operations and product risk, ensuring that every monetary transfer is screened for fraud, account takeover and unauthorized activity.
The chosen engineer will become the architect of a shared infrastructure that every risk model on the team relies upon. This includes constructing both streaming and batch feature pipelines, establishing a cross-entity data path, and delivering a unified training and evaluation framework. In addition, the role demands the design of a sub-second real-time inference serving layer that hands off predictions directly to Intuit’s decision engine, covering everything from initial risk screening to dynamic customer segmentation.
Core responsibilities and technical footprint
The position requires a clear technical vision for a platform that balances immediate performance with long-term maintainability. The engineer will design a multi-cloud infrastructure stack manage data handshakes across federated account mappings, and curate governed datasets within Intuit’s central data lake. Building a shared feature store that spans both streaming and batch workloads is essential, as is embedding observability tools that flag drift or staleness before they affect model quality.
Evaluation frameworks must be established to make model regression, production impact and The engineer will also own the end-to-end model-to-decision pathway, ensuring that model deployment, serving, and integration with the decision engine meet a strict sub-second latency budget. Setting engineering standards—testing, observability, reproducibility and operational excellence—will be part of daily work, as will structuring codebases for both agent-assisted and autonomous development.
Experience, skills and preferred background
Candidates need a minimum of eight years of production software experience, with a strong emphasis on machine-learning systems rather than pure research. A degree in Computer Science, Engineering or a comparable quantitative field (BS, MS, or PhD) is expected, though equivalent practical experience is acceptable. Deep knowledge of data structures, algorithms, distributed systems and system design is a must, alongside hands-on expertise in classification, regression, feature engineering and model evaluation.
Proficiency in Python and SQL is required, as is production experience with streaming or batch frameworks such as Spark, Flink, or their equivalents. The role demands a proven track record of owning a data or ML platform used by multiple teams, including the operational responsibilities after launch. Experience deploying models to real-time serving under hard latency constraints—preferably on AWS, SageMaker, or similar cloud services—is essential. Candidates should be comfortable managing infrastructure-as-code, CI/CD pipelines and cost-optimization without relying on ticket-based support.
While not mandatory, domain experience in risk, fraud, payments or credit—especially real-time decisioning—is highly valued. Backgrounds that include work on feature stores, entity resolution, rules engines, or regulated data handling (field-level encryption, fine-grained access control) will stand out. Strong written communication skills are crucial; the engineer must translate complex trade-offs into clear documents that align AI scientists, platform owners and risk-strategy partners.
Compensation and workplace details
Intuit offers a competitive compensation package that blends base salary, performance-based cash bonuses, equity awards and comprehensive benefits. The base salary range for this Mountain View position is $202,500 – $274,000, reflecting factors such as knowledge, skill set, experience and location. The company conducts regular pay equity reviews across gender and ethnicity to ensure fairness. Beyond monetary rewards, employees benefit from a culture that encourages continuous innovation, cross-functional collaboration and the opportunity to influence a platform that safeguards millions of financial transactions worldwide.
